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Physics-Informed Neural Networks for Higher-Order Nonlinear Schrödinger Equations: Soliton Dynamics in External Potentials

Author

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  • Leonid Serkin

    (Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad Universitaria, Circuito Exterior s/n, Coyoacán, Mexico City 04510, Mexico)

  • Tatyana L. Belyaeva

    (Universidad Autónoma del Estado de México, Av. Instituto Literario 100, Toluca 50000, Mexico)

Abstract

This review summarizes the application of physics-informed neural networks (PINNs) for solving higher-order nonlinear partial differential equations belonging to the nonlinear Schrödinger equation (NLSE) hierarchy, including models with external potentials. We analyze recent studies in which PINNs have been employed to solve NLSE-type evolution equations up to the fifth order, demonstrating their ability to obtain one- and two-soliton solutions, as well as other solitary waves with high accuracy. To provide benchmark solutions for training PINNs, we employ analytical methods such as the nonisospectral generalization of the AKNS scheme of the inverse scattering transform and the auto-Bäcklund transformation. Finally, we discuss recent advancements in PINN methodology, including improvements in network architecture and optimization techniques.

Suggested Citation

  • Leonid Serkin & Tatyana L. Belyaeva, 2025. "Physics-Informed Neural Networks for Higher-Order Nonlinear Schrödinger Equations: Soliton Dynamics in External Potentials," Mathematics, MDPI, vol. 13(11), pages 1-28, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1882-:d:1671846
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    References listed on IDEAS

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